Dynamic digital image compression based on digital image characteristics

ABSTRACT

Systems and techniques are disclosed for dynamically and automatically selecting an appropriate compression technique and/or compression parameters for digital images in order to reduce or prevent loss of significant information that may negatively impact the utility or usefulness of the digital images. For example, based on various image characteristics associated with a digital image, the system may dynamically compress the image using particular compression techniques and/or by adjusting compression parameters, to maintain significant information of the image. The system may select compression techniques and/or compression parameters based on one or more compression rules, which may be associated with image characteristics patient characteristics, medical history, etc. Further, the system may, based on the one or more compression rules, compress the image to a maximum degree of compression while maintaining the significant information of the image.

CROSS-REFERENCE TO RELATED APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are hereby incorporated by reference under 37 CFR 1.57.

This application claims benefit of U.S. Provisional Patent ApplicationNo. 62/133,738, filed Mar. 16, 2015, and titled “CLINICALLY SAFERMEDICAL IMAGE COMPRESSION BASED ON DYNAMIC SELECTION OF COMPRESSION ANDCOMPRESSION RULES.” The entire disclosure of each of the above items ishereby made part of this specification as if set forth fully herein andincorporated by reference for all purposes, for all that it contains.

TECHNICAL FIELD

Embodiments of the present disclosure relate to systems and techniquesfor dynamic image compression based on image contents.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Digital images are generally stored as digital data (also referred toherein as “image data”). For example, bitmapped or pixmapped digitalimages are represented by arrays of pixel data, where pixels of thedigital images are arranged in columns and rows. The pixel data mayinclude ranges of values representing, for example, color and/orintensity values.

Digital images may be compressed. Compression may be desirable to reducefile sizes of, and thus storage space requirements for, the digitalimages. Compression may be lossless or lossy. In lossless compression,no image data of the digital image is lost. In lossy compression, someof the image data of the digital image is lost.

SUMMARY

The systems, methods, and devices described herein each have severalaspects, no single one of which is solely responsible for its desirableattributes. Without limiting the scope of this disclosure, severalnon-limiting features will now be described briefly.

In some cases, lossy compression is desirable due to a substantialreduction in file size (as compared to lossless compression), evenconsidering the loss of image data of the digital image. However, it mayalso be desirable to ensure that the amount of, and/or type of, imagedata lost does not negatively impact the utility or usefulness of thedigital image.

For example, in certain applications lossy compression of digital imagesmay be desirable due to the large amount of image data of each digitalimage, and in view of storage and/or bandwidth requirements (e.g.,transfer of uncompressed digital images from one place to another may bevery time consuming or expensive, storage of uncompressed digital imagesmay be expensive or unfeasible, and/or decoding and/or rendering ofuncompressed digital images may be time consuming and/or use significantprocessing power). However, in these applications it may also beimportant that “significant information” of the digital images is notlost as a result of the lossy compression. What constitutes “significantinformation” of any given digital image may vary depending on a field ofapplication of the digital image, a context of the digital image, and/orthe like.

For example, in the context of medical imaging, some loss of visualinformation may be acceptable, but the lost visual information shouldnot include “clinically significant information.” For example, a digitalimage may include clinically significant information includingindications of cancer cells in the tissue of a patient. In this case,compression of the digital image should not remove or obscure theindications of cancer cells. Otherwise, the usefulness of the digitalimage (e.g., visually indicating cancer cells to someone reading thedigital image) would be lost as a result of the compression.

Studies of lossy compression of digital images in the medical imagingcontext may involve radiologists viewing a finite set of digital imagesto determine whether or not there is a perceptible difference betweenthe original and compressed versions of each digital image. Conclusionsregarding the type and degree of compression are then used by hospitalsand imaging centers to set the degree and type of image compression usedroutinely. Such conclusions drawn from these relatively small studiesare often then applied routinely to large sets of digital image data,for example, to the over one billion radiology exams performed each yearin the US alone.

There are several potential problems with previous approaches to digitalimage compression in the medical imaging field. First, conclusions drawnfrom studies of relatively small numbers of digital images may not applyto all digital images in the clinical environment, where there may be amuch wider range of image variation, e.g. related to technicalparameters and pathology. For example, a compression technique thatgenerally works well for a test group of digital images (e.g., providesno loss in clinically significant information in compressed image data),might not work well on a particular digital image of one patient'smedical imaging exam and result in a misdiagnosis in that patient. Inaddition, compression recommendations based on even a signal modality,such as CT, might not be reasonable when CT parameters and technologyevolves over time. For example, it has been demonstrated thatcompression tolerance is lower for thinner CT images.

Second, for some modalities clinical decisions are based on quantitativemeasurements (e.g., by electronic analysis) of digital imagecharacteristics. For example, pixel/signal intensity, that may not bevisually perceptible to radiologists viewing images, may beelectronically analyzed to determine clinical decisions. For example, inCT the signal intensity of pixels (also referred to herein as “pixelintensity”) in digital images may be represented in Hounsfield Units(HU), where water has 0 HU and air −1,000 HU. Most soft tissues have HUvalues above 0, with the exception of fat. In some clinical scenarios,measurement of pixel/signal intensity is used as part of the clinicalassessment of a lesion. For example, in the assessment of renal andadrenal masses, measurement of a lesion's pixel/signal intensity is usedas one of the factors to determine if the mass is likely to be benign orpotentially malignant. Compression of a CT image could result in subtlechanges in image pixel/signal intensity that might not be visuallyapparent, but could result in a small change in pixel/signal intensitythat, when measured, results in a misdiagnosis. Other examples wherequantitative measurement of image pixel/signal intensity is used forclinical diagnosis include dynamic breast MRI used for assessment ofbreast masses and measurement of SUV in PET imaging.

And third, there may be variations in the way compression techniques(e.g., lossy compression techniques) are implemented. Generally, adegree of compression can be set in two ways: (1) by setting aCompression Ratio (CR), and/or (2) by setting a Quality Factor. In someinstance, setting a fixed Compression Ratio is a problem in that loss ofquality depends on technical factors, including noise and digital imagecomplexity. While setting a constant Quality Factor could in theorymitigate these issues, there is no standard across vendors for howQuality Factor is implemented.

Accordingly, current techniques for compressing digital images, incertain fields of application, may be inadequate because significantinformation in the image data may be inadvertently lost. In the contextof medical imaging, this could potentially be dangerous, such as if lostinformation results in misdiagnoses. Therefore, a more effective andsafer method of selecting digital image compression techniques andparameters is needed.

Embodiments of the present disclosure relate to systems and techniquesfor dynamically selecting an appropriate compression technique and/orcompression parameters for digital images in order to reduce or preventloss of significant information that may negatively impact the utilityor usefulness of the digital images. For example, based on various imagecharacteristics associated with a digital image, the system maydynamically compress the image using particular compression techniquesand/or by adjusting compression parameters, to maintain significantinformation of the image. The system may select compression techniquesand/or compression parameters based on one or more compression rules,which may be associated with image characteristics, patientcharacteristics, medical history, etc. Further, the system may, based onthe one or more compression rules, compress the image to a maximumdegree of compression while maintaining the significant information ofthe image. If an acceptable compression cannot be achieved (e.g., usingany available lossy compression technique) while maintaining thesignificant information of the image, the system may compress the imageusing a lossless compression technique.

In some implementations, images may be segmented such that differentcompression (where a “compression” generally includes a compressiontechnique and compression parameters for use with the compressiontechnique) may be applied to different portions of the images. Forexample, portions of an image including significant information may bemay be compressed less than portions of an image without significantinformation.

In the context of medical imaging, the system may process a patient'smedical exam such that images of the exam are automatically compressedbased on one or more characteristics associated with the images(including, for example, characteristics of the exam and/or image seriesof the exam) and one or more compression rules, such that certainsignificant information is not lost from the images.

Accordingly, in various embodiments, large amounts of data areautomatically and dynamically calculated interactively in response touser inputs, and the calculated data (e.g., image data) may beefficiently and compactly presented to a user by the system. Thus, insome embodiments, the user interfaces described herein are moreefficient as compared to previous user interfaces in which data is notdynamically updated and compactly and efficiently presented to the userin response to interactive inputs.

Further, as described herein, the system may be configured and/ordesigned to generate user interface data useable for rendering thevarious interactive user interfaces described. The user interface datamay be used by the system, and/or another computer system, device,and/or software program (for example, a browser program), to render theinteractive user interfaces. The interactive user interfaces may bedisplayed on, for example, electronic displays (including, for example,touch-enabled displays).

Additionally, it has been noted that the design of computer userinterfaces “that are useable and easily learned by humans is anon-trivial problem for software developers.” (Dillon, A. (2003) UserInterface Design. MacMillan Encyclopedia of Cognitive Science, Vol. 4,London: MacMillan, 453-458.) The present disclosure describes variousembodiments of interactive and dynamic user interfaces that are theresult of significant development. This non-trivial development hasresulted in the user interfaces described herein which may providesignificant cognitive and ergonomic efficiencies and advantages overprevious systems. The interactive and dynamic user interfaces includeimproved human-computer interactions that may provide reduced mentalworkloads, improved decision-making, reduced work stress, and/or thelike, for a user. For example, user interaction with the interactiveuser interface via the inputs described herein may provide an optimizeddisplay of, and interaction with, image data (including digital images)and may enable a user to more quickly and accurately access, navigate,assess, and digest the image data than previous systems.

Further, the interactive and dynamic user interfaces described hereinare enabled by innovations in efficient interactions between the userinterfaces and underlying systems and components. For example, disclosedherein are improved methods of receiving user inputs (including methodsof interacting with, and selecting, images), translation and delivery ofthose inputs to various system components, automatic and dynamicexecution of complex processes in response to the input delivery,automatic interaction among various components and processes of thesystem, and automatic and dynamic updating of the user interfaces (to,for example, display the relevant digital images). The interactions andpresentation of data via the interactive user interfaces describedherein may accordingly provide cognitive and ergonomic efficiencies andadvantages over previous systems.

Various embodiments of the present disclosure provide improvements tovarious technologies and technological fields. For example, as describedabove, existing medical image interaction technology (including, e.g.,Picture Archiving and Communication Systems (“PACS”), Electronic MedicalRecord (“EMR”) Systems, and/or the like) is limited in various ways(e.g., image compression can remove significant information, imagereview is slow and cumbersome, comparison of images is inefficient,etc.), and various embodiments of the disclosure provide significantimprovements over such technology.

Additionally, various embodiments of the present disclosure areinextricably tied to computer technology. In particular, variousembodiments rely on detection of user inputs via graphical userinterfaces, calculation of updates to displayed electronic data based onthose user inputs, automatic processing of related digital images,efficient compression of digital images, and presentation of the updatesto displayed digital images via interactive graphical user interfaces.Such features and others are intimately tied to, and enabled by,computer technology, and would not exist except for computer technology.For example, the compression of digital images and interactions withdisplayed data described below in reference to various embodimentscannot reasonably be performed by humans alone, without the computertechnology upon which they are implemented. Further, the implementationof the various embodiments of the present disclosure via computertechnology enables many of the advantages described herein, includingmore efficient interaction with, and presentation of, various types ofelectronic image data, and efficient compression of image data.

According to an embodiment, a method of digital image compression isdisclosed comprising: causing execution of software instructions by oneor more hardware computing devices in order to: receive an uncompresseddigital image; determine a characteristic associated with theuncompressed digital image; access a compression rule associated withthe characteristic; compress, based on the compression rule, theuncompressed digital image to generate a first compressed digital image;determine a first amount of errors based on comparison of the firstcompressed digital image to the uncompressed digital image; compare thefirst amount of errors to an error threshold included in the compressionrule; in response to determining that the first amount of errors exceedsthe error threshold, recompress, based on the compression rule, theuncompressed digital image to generate a second compressed digital imagehaving a level of compression less than the first compressed digitalimage; and determine a second amount of errors based on comparison ofthe second compressed digital image to the uncompressed digital image.

According to an aspect, the characteristic associated with theuncompressed digital image includes at least one of: an imagingmodality, an anatomical feature, or an acquisition type.

According to another aspect, the compression rule indicates acompression algorithm and a first quality factor, and the firstcompressed digital image is generated based on the compression algorithmand the first quality factor.

According to yet another aspect, the compression rule indicates a secondquality factor that is greater than the first quality factor, and thesecond compressed digital image is generated based on the compressionalgorithm and a second quality factor.

According to another aspect, determining the first amount of errorscomprises: causing execution of software instructions by one or morehardware computing devices in order to: determine a difference betweenthe uncompressed digital image and the first compressed digital image togenerate difference image data; and determine the first amount of errorsby at least one of: determining a number of pixels in the differenceimage data having a value indicative of an error, or determining adegree of error in one or more pixels of the difference image data.

According to yet another aspect, determining the first amount of errorscomprises: causing execution of software instructions by one or morehardware computing devices in order to: identify one or more pixels ofthe uncompressed digital image having an intensity value satisfying athreshold; for each pixel of the one or more pixels, determine adifference between the pixel of the uncompressed digital image and thepixel of the first compressed digital image to generate difference imagedata; and determine the first amount of errors by at least one of:determining a number of pixels in the difference image data having avalue indicative of an error, or determining a degree of error in one ormore pixels of the difference image data.

According to another aspect, each of the one or more pixels comprises agroup of pixels, and wherein each group of pixels comprises at least oneof: a 4×4 group of pixels, or a 6×6 group of pixels.

According to yet another aspect, determining the first amount of errorscomprises: causing execution of software instructions by one or morehardware computing devices in order to: identify one or more regions ofthe uncompressed digital image having pixel intensity values satisfyinga threshold; for each pixel or group of pixels of the one or moreregions, determine a difference between the pixel of the uncompresseddigital image and the pixel of the first compressed digital image togenerate difference image data; and determine the first amount of errorsby at least one of: determining a number of pixels in the differenceimage data having a value indicative of an error, or determining adegree of error in one or more pixels of the difference image data.

According to another aspect, the method further comprises: causingexecution of software instructions by one or more hardware computingdevices in order to: store the second compressed digital image in a datastore.

According to yet another aspect, the method further comprises: causingexecution of software instructions by one or more hardware computingdevices in order to: in response to determining that the second amountof errors exceeds the error threshold defined by the compression rule,recompress the uncompressed digital image using a lossless compressiontechnique to generate a third compressed digital image; and store thethird compressed digital image in a data store.

According to another embodiment, a method of digital image compressionis disclosed comprising: causing execution of software instructions byone or more hardware computing devices in order to: receive anuncompressed digital image; determine a characteristic associated withthe uncompressed digital image; access a compression rule associatedwith the characteristic; compress, based on the compression rule, theuncompressed digital image using each of a plurality of compressionlevels to generate a set of compressed digital images that are eachcompressed at different compression levels; for each of the compresseddigital images of the set, determine a respective amount of errors basedon comparison of the respective compressed digital images to theuncompressed digital image; and determine a first compressed digitalimage of the set that: is associated with an amount of errors thatsatisfies a threshold defined by the compression rule, and is compressedat a highest compression level of the plurality of compression levelsthat has an amount of errors that satisfies the threshold defined by thecompression rule.

According to an aspect, the characteristic associated with theuncompressed digital image includes at least one of: an imagingmodality, an anatomical feature, or an acquisition type.

According to another aspect, the plurality of compression levels eachincludes a compression algorithm and a set of respective qualityfactors, and the set of compressed digital images is generated based onthe respective compression algorithms and quality factors.

According to yet another aspect, determining an amount of errors bycomparison of a compressed digital image to the uncompressed digitalimage comprises: causing execution of software instructions by one ormore hardware computing devices in order to: determine a differencebetween the uncompressed digital image and the compressed digital imageto generate difference image data; and determine the amount of errors byat least one of: determining a number of pixels in the difference imagedata having a value indicative of an error, or determining a degree oferror in one or more pixels of the difference image data.

According to another aspect, determining an amount of errors bycomparison of a compressed digital image to the uncompressed digitalimage comprises: causing execution of software instructions by one ormore hardware computing devices in order to: identify one or more pixelsof the uncompressed digital image having an intensity value satisfying athreshold; for each pixel of the one or more pixels, determine adifference between the pixel of the uncompressed digital image and thepixel of the compressed digital image to generate difference image data;determine the amount of errors by at least one of: determining a numberof pixels in the difference image data having a value indicative of anerror, or determining a degree of error in one or more pixels of thedifference image data.

According to yet another aspect, each of the one or more pixelscomprises a group of pixels, and wherein each group of pixels comprisesat least one of: a 4×4 group of pixels, or a 6×6 group of pixels.

According to another aspect, determining an amount of errors bycomparison of a compressed digital image to the uncompressed digitalimage comprises: causing execution of software instructions by one ormore hardware computing devices in order to: identify one or moreregions of the uncompressed digital image having pixel intensity valuessatisfying a threshold; for each pixel or group of pixels of the one ormore regions, determine a difference between the pixel of theuncompressed digital image and the pixel of the compressed digital imageto generate difference image data; and determine the amount of errors byat least one of: determining a number of pixels in the difference imagedata having a value indicative of an error, or determining a degree oferror in one or more pixels of the difference image data.

According to yet another aspect, the method further comprises: causingexecution of software instructions by one or more hardware computingdevices in order to: store the first compressed digital image in a datastore.

According to another aspect, the method further comprises: causingexecution of software instructions by one or more hardware computingdevices in order to: in response to determining that none of thecompressed digital images of the set is associated with an amount oferrors that satisfies the threshold defined by the compression rule,recompress the uncompressed digital image using a lossless compressiontechnique to generate a second compressed digital image; and store thesecond compressed digital image in a data store.

According to yet another aspect, the highest compression level is acompression level that requires a least amount of storage space ascompared to other compression levels of the plurality of compressionlevels, while having the amount of errors that satisfies the thresholddefined by the compression rule.

According to yet another embodiment, a method of digital imagecompression is disclosed comprising: causing execution of softwareinstructions by one or more hardware computing devices in order to:receive an uncompressed digital image; determine a characteristicassociated with the uncompressed digital image; access a compressionrule associated with the characteristic; compress, based on thecompression rule, the uncompressed digital image using each of aplurality of compression levels to generate a set of compressed digitalimages that are each compressed at different compression levels;generate, based on the compression rule, a respective correction imagefor each of the compressed digital images of the set, wherein thecorrection images, when combined with their respective compresseddigital images, removes errors from at least a portion of the respectivecompressed digital images; and determine a combination of a firstcompressed digital image and an associated first correction image of theset that requires a minimum amount of storage space.

According to an aspect, the characteristic associated with theuncompressed digital image includes at least one of: an imagingmodality, an anatomical feature, or an acquisition type.

According to another aspect, the plurality of compression levels eachincludes a compression algorithms and a set of respective qualityfactors, and the set of compressed digital images is generated based onthe respective compression algorithms and quality factors.

According to yet another aspect, the method further comprises: causingexecution of software instructions by one or more hardware computingdevices in order to: identify one or more regions of the uncompresseddigital image having pixel intensity values satisfying a threshold; anddesignate the one or more regions as the portion.

According to another aspect, the one or more regions are identifiedbased on one or more segmentation rules.

According to yet another aspect, the method further comprises: causingexecution of software instructions by one or more hardware computingdevices in order to: losslessly compress the first associated correctionimage.

According to another aspect, the method further comprises: causingexecution of software instructions by one or more hardware computingdevices in order to: store or transmit the combination of the firstcompressed digital image and the associated first correction image thatis losslessly compressed.

According to yet another aspect, the method further comprises: causingexecution of software instructions by one or more hardware computingdevices in order to: combine the first compressed digital image and thefirst associated correction image into a single compressed digitalimage.

According to another aspect, the correction images, when combined withtheir respective compressed digital images, removes all errors from therespective compressed digital images and results in the uncompresseddigital image.

Additional embodiments of the disclosure are described below inreference to the appended claims, which may serve as an additionalsummary of the disclosure.

In various embodiments, computer systems are disclosed that comprise oneor more hardware computer processors in communication with one or morenon-transitory computer readable storage devices, wherein the one ormore hardware computer processors are configured to execute theplurality of computer executable instructions in order to cause thecomputer system to perform operations comprising one or more aspects ofthe above-described embodiments (including one or more aspects of theappended claims).

In various embodiments, computer-implemented methods are disclosed inwhich, under control of one or more hardware computing devicesconfigured with specific computer executable instructions, one or moreaspects of the above-described embodiments (including one or moreaspects of the appended claims) are implemented and/or performed.

In various embodiments, non-transitory computer-readable storage mediumsstoring software instructions are disclosed, wherein, in response toexecution by a computing system having one or more hardware processors,the software instructions configure the computing system to performoperations comprising one or more aspects of the above-describedembodiments (including one or more aspects of the appended claims).

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings and the associated descriptions are provided toillustrate embodiments of the present disclosure and do not limit thescope of the claims. Aspects and many of the attendant advantages ofthis disclosure will become more readily appreciated as the same becomebetter understood by reference to the following detailed description,when taken in conjunction with the accompanying drawings, wherein:

FIG. 1 is a block diagram showing various aspects of a computing systemand network environment in which the computing system may beimplemented, according to various embodiments of the present disclosure;

FIG. 2 is a diagram illustrating various example compressed versions ofan example digital image, according to an embodiment of the presentdisclosure;

FIGS. 3A-3B are diagrams illustrating various aspects of various examplecompressed versions of an example digital image, according to anembodiment of the present disclosure;

FIG. 4 is a diagram illustrating an example automated segmentation of adigital image, according to an embodiment of the present disclosure;

FIGS. 5A-5C are diagrams illustrating example compression rules,according to various embodiments of the present disclosure;

FIGS. 6-8 are flowcharts illustrating example methods of compressingdigital images, according to various embodiments of the presentdisclosure; and

FIG. 9 is a diagram illustrating various example compressed versions ofan example digital image, according to an embodiment of the presentdisclosure.

DETAILED DESCRIPTION

Although certain preferred embodiments and examples are disclosed below,inventive subject matter extends beyond the specifically disclosedembodiments to other alternative embodiments and/or uses and tomodifications and equivalents thereof. Thus, the scope of the claimsappended hereto is not limited by any of the particular embodimentsdescribed below. For example, in any method or process disclosed herein,the acts or operations of the method or process may be performed in anysuitable sequence and are not necessarily limited to any particulardisclosed sequence. Various operations may be described as multiplediscrete operations in turn, in a manner that may be helpful inunderstanding certain embodiments; however, the order of descriptionshould not be construed to imply that these operations are orderdependent. Additionally, the structures, systems, and/or devicesdescribed herein may be embodied as integrated components or as separatecomponents. For purposes of comparing various embodiments, certainaspects and advantages of these embodiments are described. Notnecessarily all such aspects or advantages are achieved by anyparticular embodiment. Thus, for example, various embodiments may becarried out in a manner that achieves or optimizes one advantage orgroup of advantages as taught herein without necessarily achieving otheraspects or advantages as may also be taught or suggested herein.

I. Overview

As mentioned above, current techniques for compressing digital images,in certain fields of application, may be inadequate because significantinformation in the image data may be inadvertently lost. In the contextof medical imaging, this could potentially be dangerous and result inmisdiagnoses. Systems and techniques of the present disclosure overcomecertain problems with the current techniques.

In particular, embodiments of the present disclosure relate to systemsand techniques for dynamically and automatically selecting anappropriate compression technique and/or compression parameters fordigital images in order to reduce or prevent loss of significantinformation that may negatively impact the utility or usefulness of thedigital images. For example, based on various image characteristicsassociated with a digital image, the system may dynamically compress theimage using particular compression techniques and/or by adjustingcompression parameters, to maintain significant information of theimage. The system may select compression techniques and/or compressionparameters based on one or more compression rules, which may beassociated with image characteristics, patient characteristics, medicalhistory, etc. Further, the system may, based on the one or morecompression rules, compress the image to a maximum degree of compressionwhile maintaining the significant information of the image. If anacceptable compression cannot be achieved (e.g., using any availablelossy compression technique) while maintaining the significantinformation of the image, the system may compress the image using alossless compression technique.

In some implementations, images may be segmented such that differentcompression (where a “compression” generally includes a compressiontechnique and compression parameters for use with the compressiontechnique) may be applied to different portions of the images. Forexample, portions of an image including significant information may becompressed less than portions of an image without significantinformation.

In the context of medical imaging, the system may process a patient'smedical exam such that images of the exam are automatically compressedbased on one or more characteristics associated with the images(including, for example, characteristics of the exam and/or image seriesof the exam) and one or more compression rules, such that certainsignificant information is not lost from the images.

In the context of medical imaging, examples of image characteristicsbased upon which compression rules may be selected include:

-   -   Modality. For example, for CT images, compression rules may take        into account strict requirements with regard to change in        pixel/signal intensity (because, for example, significant        information for CT images may include quantitative signal        intensity measurements). In another example, for chest        radiography, compression rules may take into account that        pixel/signal intensity is arbitrary and not used for        quantitative measurement (and thus, signal intensity is not part        of the significant information of chest radiography images).    -   Acquisition type within a modality. For example, for breast MRI        images, compression rules may take into account that, for some        image series (e.g. dynamic enhanced series) relative changes in        pixel/signal intensity may be important (e.g., include        significant information), while for other image series (e.g.,        anatomic series) pixel/signal intensity is not as important        (e.g., other visual assessment information is significant        information).    -   Whether or not images are to be combined into a multiplanar        reformatted images or 3D volumetric images. For example, in        these cases the compression rules may take into account that it        might be important that the same compression parameters be        applied to every image in a series.

In some implementations, compression rules may further be selected basedon other characteristics, such as patient characteristic, medicalhistory of a patient, user characteristics, etc. For example, certaincompression rules may be associated with particular users, groups ofusers, sites, etc., such that different compression rules may be applieddepending on who is viewing the images and/or where the images are beingviewed. For example, one user may prefer less compressed images thananother user, thus the user may specify a particular set of compressionrules that are specific to that user.

Accordingly, in some cases, in the context of medical imaging, thesystem, based on the compression rules, may select a single compressiontechnique (e.g., compression algorithm and associated compressionparameters) to apply to all images in a series of a medical exam. Forexample, a lowest degree of compression required for any image in aseries or exam (e.g., a degree of compression that ensures that allimages in the series or exam maintain significant information) may betested against all images in the series or exam to ensure that allimages pass a quality assessment (e.g., that significant information isretained in all images of the series or exam). If one or more imagesfail the quality assessment, then other compression settings may betried until one is found that allows all the images in the series orexam to pass the quality assessment.

While systems and techniques of the present disclosure are described andillustrated in the context of medical imaging, these systems andtechniques may be applied to various other fields of application. Forexample, the systems and techniques may be applied in the fields ofaircraft failure detection, law enforcement, semiconductor fabrication,and/or the like. Further, the systems and techniques may be applied toother types of data other than digital images. For example, the systemsand techniques may be applied to compression of video data, sound data,text/file data, and/or the like.

Embodiments of the disclosure will now be described with reference tothe accompanying figures, wherein like numerals refer to like elementsthroughout. The terminology used in the description presented herein isnot intended to be interpreted in any limited or restrictive manner,simply because it is being utilized in conjunction with a detaileddescription of certain specific embodiments of the disclosure.Furthermore, embodiments of the disclosure may include several novelfeatures, no single one of which is solely responsible for its desirableattributes or which is essential to practicing the embodiments of thedisclosure herein described.

II. Terms

In order to facilitate an understanding of the systems and methodsdiscussed herein, a number of terms are defined below. The terms definedbelow, as well as other terms used herein, should be construed broadlyto include the provided definitions, the ordinary and customary meaningof the terms, and/or any other implied meaning for the respective terms.Thus, the definitions below do not limit the meaning of these terms, butonly provide exemplary definitions.

User: Also referred to herein as “reviewer” and/or “viewer.” Anindividual (or group of individuals) that interfaces with a computingdevice to, for example, view digital images. In the context of medicalimaging, users may include, for example, physicians (including, forexample, doctors, radiologists, etc.), hospital staff, and/or any otherindividuals (including persons not medically trained) involved inviewing, analysis, annotation, comparison, acquisition, storage,management, or other tasks related to digital images as describedherein.

User Input (also referred to as “Input”): As used herein in reference touser interactions with data displayed by a computing system, “userinput” is a broad term that refers to any type of input provided by auser that is intended to be received and/or stored by the system, tocause an update to data that is displayed by the system, and/or to causean update to the way that data is displayed by the system. Non-limitingexamples of such user input include keyboard inputs, mouse inputs,digital pen inputs, voice inputs, finger touch inputs (e.g., via touchsensitive display), gesture inputs (e.g., hand movements, fingermovements, arm movements, movements of any other appendage, and/or bodymovements), and/or the like. Additionally, user inputs to the system mayinclude inputs via tools and/or other objects manipulated by the user.For example, the user may move an object, such as a surgical instrument,tool, stylus, or wand, to provide inputs. Further, user inputs mayinclude motion, position, rotation, angle, alignment, orientation,configuration (e.g., fist, hand flat, one finger extended, etc.), and/orthe like. For example, user inputs may comprise a position, orientation,and/or motion of a hand and/or a 3D mouse.

Digital Image (also referred to as an “Image”): Any collection ofdigital data (also referred to herein as “image data”) that may berendered visually. Digital images may be acquired via various methods,including by images sensors (such as CCD, CMOS, NMOS, etc.), microscopy(e.g., optical, scanning probe, electron, etc.), optical coherencetomography (OCT), radiography (e.g., x-ray), computed tomography (CT),magnetic resonance imaging (MRI), Ultrasound (US), positron emissiontomography scan (PET), nuclear scan (NM), etc. In the context of medicalimaging, digital images may include, for example, any type of digitalimage of an organism (e.g., a human patient). Common types of digitalimages in the field of medical imaging include but are not limited toradiograph images (e.g., an x-ray image), computed tomography (CT)images, magnetic resonance imaging (MRI) images, Ultrasound (US) images,mammogram images, positron emission tomography scan (PET) images,nuclear scan (NM) images, pathology images, endoscopy images,ophthalmology images, or many other types of digital images. Digitalimages, particularly in medical imaging, may be reconstructed and/orrendered from 3D or volumetric image data using methods includingmultiplanar reformation/reconstruction (MPR), maximum intensityprojection (MIP), and/or the like (including, e.g., any ComputerizedAdvanced Processing (CAP), as described below).

Compressed Digital Image (also referred to as a “Compressed Image”): Adigital image that has been compressed via either a lossless or a lossycompression technique (e.g., a compression algorithm) into a compressedformat. Examples of common compression formats include BMP, TIFF, JPEG,GIF, and PNG. Any given compression technique may include one or more“compression parameters” that may affect an amount of, or type of,compression that is applied by the compression technique. As describedbelow, one compression parameter than may be applicable to certaincompression techniques is a “quality factor”.

Modality: A medical imaging method (e.g., a patient who undergoes an MRIis said to have been scanned with the MRI modality).

Digital Image Series (also referred to as a “Series”): Any two or moredigital images that are related. Digital images in a series typicallyshare one or more common characteristics. For example, in the context ofmedical imaging, such common characteristics may include a type ofanatomic plane and/or an image orientation. For example, a digital imageseries may comprise two or more digital images of a particular patientthat are acquired on a particular date, e.g., different x-rayprojections of the chest. A series of contiguous 3 mm axial CT scans ofthe chest is another example of a digital image series. A brain MRI scanmight include the following series: sagittal T1 weighted images, axialT1 weighted images, axial FLAIR images, axial T2 weighted images, aswell as post contrast axial, sagittal and coronal T1 weighted series.

Medical Imaging Exam (also referred to as a “Medical Exam” and/or an“Exam”): A collection of data related to an examination of a patient.May be specific to a particular time or time period. Generally includesone or more digital images and/or image series, reports, notes, graphs,measurements, annotations, videos, sounds or voice data, diagnoses,and/or other related information. May include multiple image series ofmultiple modalities, volumetric imaging data, reconstructed imagesand/or rendered images. For example, an exam of a patient may be thebrain MRI scan mentioned above, and may include each of the image seriesobtained on a particular date including: sagittal T1 weighted images,axial T1 weighted images, axial FLAIR images, axial T2 weighted images,as well as post contrast axial, sagittal and coronal T1 weighted series.Another example of an exam may be a dual-energy radiography exam, whichmay include image data including traditional x-ray image images, bonesubtracted (or “bone out”) x-ray images, and/or tissue subtracted (or“tissue out”) x-ray images.

Significant Information: Any information conveyed by a digital imagethat is relevant to the utility or usefulness of the digital image. Whatconstitutes “significant information” of any given digital image mayvary depending on one or more “image characteristics” of the digitalimage. In the context of medical imaging, “significant information” mayalso be referred to as “clinically significant information” as thesignificant information may relate, e.g., to clinical diagnoses of apatient. For example, a digital image may include clinically significantinformation including indications of cancer cells in the tissue of apatient. In some instances significant information may be detectedvisually, e.g., may be viewable by a user when the image is visuallyrendered. In other instances, significant information may be detected byanalysis of image data (including, e.g., by any Computerized AdvancedProcessing (CAP), as described below). For example, image pixelintensity values may be analyzed to determine clinically significantinformation, as mentioned above.

Image Characteristic: Any characteristic related to a digital image.Image characteristics may include, for example, a field of applicationof the digital image (e.g., medical imaging, aircraft failure detection,law enforcement, semiconductor fabrication, etc.), a type of (or methodof acquisition of) the digital image (e.g., CT scan, OCT, CCD, etc.), acontext of the digital image (e.g., patient diagnosis, airplane winganalysis, license plate reading, face detection, chip analysis,satellite image, etc.), content of the digital image (e.g., CT scan ofbrain, portion of airplane wing, license plate, face, chip transistors,terrain, etc.), features of interest in the digital image (e.g., cancercells, micro-fractures in metal, license plate numbers/letters, facialfeatures, transistor defects, roads, etc.), a user/group/site/etc. thatmay view the digital image or where the digital image may be viewed,and/or the like. Additional examples of image characteristics that maybe relevant to the medical imaging context include, for example, imagemodality (e.g., CT, MRI, radiography, etc.), acquisition type (e.g.,dynamic enhanced MRI, anatomic MRI, etc.), image angle (e.g., an angleof an image with reference to a standard one or more planes of humananatomy; also referred to herein as “scan plane”), anatomical position(and/or location) (e.g., a location, with reference to a standard one ormore planes of human anatomy, of the patient represented in a particularimage), image orientation (e.g., an orientation of the image withreference to a standard one or more planes of human anatomy), imagerotation (e.g., a rotation of the image with reference to a standard oneor more planes of human anatomy), image field of view, slice thickness,image window and/or level (e.g., a contrast of the image, a brightnessof the image, and/or the like), image color map (e.g., that includesinformation for rendering different pixel intensities as differentcolors), other color characteristics, image opacity (and/or opacitymap), image zoom level, image cropping information, and/or the like. Insome instances, image characteristics may further includecharacteristics associated with a series or exam (e.g., clinicalindication, patient characteristics, etc.). In some instances, imagecharacteristics may include information about intended processing of theimage. For example, the image characteristics may include informationabout whether or not images are to be combined into a multiplanarreformatted images or 3D volumetric images. In some instances, one ormore image characteristics may be user defined and/or based on userpreferences (and/or group/site preferences). These image characteristicsare provided for illustrative purposes only, as such (and other)characteristics may be grouped, separated, and/or combined differentlythan described above.

Computerized Advanced Processing (CAP): Any computerized analysis,analysis technique, and/or processing technique discussed herein, and/orany similar computerized processing technique that is currently or lateravailable. CAP and the systems and methods described herein may beapplied in various areas including, but not limited to, various types ofcaptured digital images (for example, in the context of medical imaging:cardiology, dermatology, pathology and/or endoscopy, among others; inother contexts: surveillance imaging, satellite imaging, and the like),computer generated digital images (for example, in the context ofmedical imaging: 3D images from virtual colonoscopy, 3D images ofvessels from CTA, and the like), as well as non-imaging data includingaudio, text, and numeric data. In some embodiments, CAP may include, butis not limited to, significant information detection, image featuredetection, image segmentation, pixel intensity analysis, volumerendering (including, for example, multiplanarreformation/reconstruction (MPR), maximum intensity projection (MIP), 3Dvolume rendering, and/or 3D surface rendering), graphicalprocessing/reporting (e.g., automated identification and outlining oflesions, lumbar discs etc.), automated measurement of lesions or otheranatomical features, other image processing techniques, and/or the like.

III. Example Computing Devices and Systems

FIG. 1 is a block diagram showing various aspects of a computing system150 and network environment 100 in which the compression computingsystem 150 may be implemented, according to various embodiments of thepresent disclosure. The compression computing system 150 may be referredto herein as the “computing system,” the “system,” and/or the like.

As shown, the network environment 100 may include the computing system150, a computer network 190, an image server 120, a compression rulesdatabase 124, a rules engine 163, one or more imaging devices orscanners 110, a Picture Archive and Communication System (PACS) 121,and/or a PACS Workstation 122.

As described below, in various embodiments the computing system 150, theimage server 120, the compression rules database 124, the rules engine163, the one or more imaging devices or scanners 110, the PictureArchive and Communication System (PACS) 121, and/or the PACS Workstation122 may be in communication with one another via the computer network190. In some embodiments, various of the image server 120, thecompression rules database 124, the rules engine 163, the one or moreimaging devices or scanners 110, the Picture Archive and CommunicationSystem (PACS) 121, and/or the PACS Workstation 122 may or may not beconsidered a part of the computing system 150. For example, in someembodiments one or more of these components may be implemented as partof the computing system 150, may be in direct communication with thecomputing system 150, and/or may be in indirect communication (e.g.,over network 190) with the computing system 150.

The computing system 150 may include various components as shown anddescribed in detail below. As described below, the computing system 150may display digital images (including, e.g., medical images) and/orother data to a user via a display 155. The computing system 150 mayinclude one or more input devices 156 that detect input from a user asdescribed below. As described below, the computing system 150 maydisplay user interfaces, digital images, and/or the like, to a user viaa display 155. Further, user input may be received via the computingsystem 150, for example selection of exams, images, compressionsparameters and/or the like, in response to which the informationdisplayed may be updated.

Additional components of the computing system 150 may include, forexample, one or more processors 152 and memory and/or data storage 153(including one or more software modules 151 and/or a rules engine 163(which may itself comprise a software module)). In particular, asdescribed below, the rules engine 163 may execute various rules (forexample, one or more rules stored in the compression rules database 124)that may be used to compress digital images based on compression rulesand image characteristics, translate various user inputs intocorresponding changes of displayed images and/or other data, and/or thelike.

“Compression rules” are described above and below, and include any rulesof the compression rules database 124 that may be executed by the rulesengine 163 to determine compression of data, including image data.Compression rules may be associated with one or more imagecharacteristics, for example, to indicate compression techniques and/orcompression parameters, among other aspects as described herein.Further, as described above, compression rules may be associated withparticular users, user groups, sites, etc. Examples of compression rulesare described below in references to FIGS. 5A-5C.

“Segmentation rules” are described below, and include any rules of thesystem that may be executed by the rules engine 163 to determinesegmentation of data, including image data. Segmentation rules may beassociated with image characteristics, for example, to determineparticular portions of an image to be compressed differently, amongother aspects as described herein. Further, segmentation rules may beassociated with particular users, user groups, sites, etc. (as describedherein). Examples of segmentation rules are described below inreferences to FIG. 4.

In various embodiments, any of the rules of the compression rulesdatabase 124 (including, e.g., compression rules) may be selected basedon, for example, one or more image characteristics of image data and/oran identifier or characteristic associated with a user. In variousembodiments, any rules and/or particular sets of rules of thecompression rules database 124 may be associated with specific users,groups of users (e.g., a type of doctor, etc.), sites (e.g., a hospital,etc.), other characteristics of users, computing devices used the users,and/or the like. Thus, rules may be automatically selected by the systembased on one or more characteristics associated with a user. In someembodiments, a default set of rules may apply to all user interactions,and/or when there are no rules specifically associated with the user.The various rules may be provided by the users themselves, by a systemadministrator, and/or they may be preprogrammed in to the system.

As further described below, network environment 100 may include a server120 that provides information that is displayed by computing system 150.The server 120 may also include image storage (for example, a datastore, database, and/or storage system) that may be configured to storeinformation, such as image data, that is processed by server 120 and/orcomputing system 150. In various embodiments, image data is stored inDigital Imaging and Communications in Medicine (“DICOM”) format and/orany other appropriate format.

The one or more imaging devices/scanners 110 may acquire image data(e.g., digital images, medical imaging data, digital image series, etc.)to be processed by the system and displayed to a user. Imagingdevices/scanners 110 may include scanners of a variety of technologies,for example, computed tomography (CT), magnetic resonance imaging (MRI),ultrasounds, nuclear medicine, positron emission computed tomography(PET), radiography, mammography, and/or the like. Additional examplesand details of the imaging devices/scanners 110 are described abovebelow.

The network environment 100 also includes the PACS 121 that may be usedto manage medical imaging exams, as described in further detail below.

IV. Examples of Digital Image Compression and Analysis

FIG. 2 is a diagram illustrating various example compressed versions ofan example digital image, according to an embodiment of the presentdisclosure. As shown, FIG. 2 includes two rows, each containing fourimages. The first row displays compressed versions of an original image310 of FIG. 3. The images are compressed with a lossy JPEG 2000compression algorithm using four different quality factors (which arealso referred to herein as “compression parameters”), displayed as Q=70to Q=100 on the top each image. Also listed is the resulting compressionratio, ranging from 37:1 to 6:1. The loss of information resulting fromthe lower quality factors and resulting higher compression ratios isvisually apparent.

The second row of four images displays difference images, where theoriginal image 310 is subtracted from each compressed image so that theerror related to compression is displayed for each pixel.

Note that for the Q=70, the difference image demonstrates significantstructure as a result of the differences, or “errors”, in the compressedimage relative to the original image 310. The difference image for theQ=100 image displays very little structure as the difference between theoriginal and Q=100 compressed image is low. The intermediate imagesdemonstrate intermediate degrees of error in the difference images.

Note that these difference images could also be considered “correction”images as they could be subtracted from (or added, if inverted) to thelossy compressed images to correct losses related to compression. Insome embodiments, corrections can be applied to some pixels based oncompression rules and/or CAP, for example based on anatomic structure,regions, or signal intensity, to cause the lossy compressed image toachieve the desired degree of quality (e.g., to make sure clinicallysignificant information is included in the compressed images).

FIGS. 3A-3B are diagrams illustrating various aspects of various examplecompressed versions of an example digital image 310, according to anembodiment of the present disclosure. FIG. 3A illustrates the original,uncompressed image 310. Superimposed on the image is an oval thatdefines an area for analysis.

Graph 320 is a histogram which illustrates the results of an analysis ofJPEG 2000 lossy compressed images of the original image performed withthree different quality factors, Q=80, Q=90 and Q=100 (as illustrated inFIG. 2). The histogram displays a measure of the difference between theoriginal image and the compressed versions, e.g., the error in pixelintensities due to compression. In one embodiment, errors in pixelintensities are calculated for each pixel, as a difference between thepixel intensity in the original uncompressed image 310 and the pixelintensity in a decompressed image generated from the compressed imagedata. Thus, for an image with 1,048,576 pixels (e.g., a 1024×1024image), pixel errors may be calculated for each of the 1,048,576 pixels.Graph 320 shows the distribution of these pixels errors. In particular,in graph 320 an error of 0 on the x-axis indicates the number of pixelsin the compressed version that are identical in value to the originalimage. Values to the right and left of 0 display the number of pixelsthat have for example an error of 1, −1, 2, −2, . . . etc.

Note that for Q=90 compared to Q=100 there are a greater number ofpixels that have an error and that the errors are greater. For Q=80, thenumber of errors and magnitude of the errors are greater. Thisillustrates one problem with lossy compression, in which the signalintensity of pixels in the lossy compressed image differs from theoriginal image.

FIG. 3B shows further detail of errors for the lossy JPEG 2000compressed images discussed with reference to FIG. 3A. Diagram 330 is ahistogram of compression errors for the image compressed with a qualityfactor of Q=80, including the mean, minimum, and maximum errors.Diagrams 332 and 334 show results for Q=90 and Q=100 compressed images.

In various implementations, errors may be determined, and/or correctionimages may be generated, on a pixel-by-pixel basis, and/or based onmultiple pixels or groups of pixels at a time, e.g., 4×4 groups ofpixels, 6×6 groups of pixels, or the like. Any image processing,compression, generation, segmentation, error determination, and/oranalysis (and/or the like) described herein may similarly be performedon a pixel-by-pixel basis, and/or based on multiple pixels or groups ofpixels at a time, e.g., 4×4 groups of pixels, 6×6 groups of pixels, orthe like.

V. Example Image Segmentation

FIG. 4 is a diagram illustrating an example automated segmentation of adigital image, according to an embodiment of the present disclosure.Automated segmentation may be used to detect various regions within animage so that rules (e.g., compression rules) may treat differentregions in an image differently. In some embodiments, rules may apply tospecific tissues, structures, or signal intensity ranges, and in thoseembodiments automated determination of tissues, structure or signalintensity ranges within image may be used to identify such regions. Asmentioned above, automated segmentation may be performed by one or moreCAP.

In the example of FIG. 4, an example brain CT image is automaticallysegmented into six different regions that could be used in compressionquality rules. Image 410 is an example original, uncompressed image.Image 420 is an image in which different tissues, structures, or signalintensity regions, defined by pixel intensities, have been automaticallydetermined. In the example of image 420, each pixel within the originalimage was automatically examined and compared with image segmentationrules to determine which tissue type it should be assigned.

Table 430 is an example of image segmentation rules for six tissuetypes. In the example of Image Segmentation Rules 430, pixels withintensities in the range of −20,000 to −300 are considered to be air andassigned a gray color in the segmented image 420. Pixels in the range of−3 to 13 are assigned to be CSF and displayed in red in the segmentedimage. Pixels in the range of 101 to 20,000 are assigned to be bone anddisplayed in yellow. Information for fat, brain, and calcification arealso shown in the segmentation rules and the resulting segmented image420.

In other embodiments, other systems for segmentation could be utilized,for example involving 2D or 3D region growing or anatomic templates.

Table 435 displays information about the regions that have beensegmented within image 410.

The original image segment statistics displays the minimum, maximum, andaverage pixel intensity within the automatically segmented images, aswell as the number of pixels within each segmented region. In addition,errors related to compression within the segmented regions are displayedfor images compressed with a JPEG 2000 lossy compression algorithm forquality factors (Q) of 70, 80, 90, and 100. For each of those qualityfactors, statistics are listed for the compression errors, thedifference between the original image and the lossy compressed image.Specifically, the range of the error is listed as the minimum andmaximum difference between the original and compressed image, as well asthe average error. For example, for the Q=80 image, the average error inthe CSF region is 2, with individual pixel errors within that regionranging from −34 to 42.

Various methods may be used to segment digital images in the medicalimaging context. Examples of such methods are disclosed in the followingin journal article: Evan K. Fram, J. David Godwin, and Charles E.Putman, Three-Dimensional Display of the Heart, Aorta, Lungs, and AirwayUsing CT, American Journal of Roentgenology, 139: 1171-1176, December1982, which is hereby incorporated by reference herein in its entirety.For example, the article describes software that automatically segmentstissues to isolate organs (and create 3D displays) of the organs. Thesegmentation methods discussed in the article, as well as any othercurrently-known or later developed segmentation techniques may be usedin conjunction with the systems and methods discussed herein.

VI. Example Compression Rules

FIGS. 5A-5C are diagrams illustrating example compression rules,according to various embodiments of the present disclosure. As describedabove, compression rules may be associated with one or more imagecharacteristics.

In reference to FIG. 5A, compression rules are shown that are associatedwith exam types (e.g., modality, acquisition type, and/or anatomicalfeature). For example, table 501 shows a list of exam types. In oneembodiment the list may include other image characteristics, such asclinical information, so that rules may, for example, be specific forvarious clinical indications (e.g. CT of Brain to evaluate brain tumor),users, groups, imaging parameters, particular imaging devices, etc.

In the example shown, entry 502, “CT of Brain,” is associated withexample rules consisting of Sequence of Compression table 504 listingcompression parameters in the order they are to be tested, as well asexample Quality Rules table 506, listing quality requirements for thecompressed image.

The application of these rules is discussed with reference to theembodiment of FIG. 6.

FIG. 5B is another example of compression rules, similar to the exampleof FIG. 5A, but associated with entry 512 for CT of Abdomen. Inaddition, the rules include a Compression Set table 514, rather than aSequence of Compression table as in the example of FIG. 5A. In thisexample, multiple compression algorithms are used in order to identifythe optimal compression for images. In other embodiments, evenadditional compression techniques and/or parameters for thosecompression techniques may be included in rules for one or more examtypes and/or other image characteristics.

The example rules illustrated in FIG. 5B will be discussed withreference to the embodiment of FIG. 7.

FIG. 5C is another example of compression rules. The example rulesillustrated in FIG. 5C will be discussed with reference to theembodiment of FIG. 8.

VII. Example Methods

FIGS. 6-8 are flowcharts illustrating example methods of compressingdigital images, according to various embodiments of the presentdisclosure. The methods of FIGS. 6-8, as well as other processesdiscussed herein, may be performed, e.g., by the computing system 150,such as by accessing images stored on the image server 120, andexecuting compression rules stored in the compression rules database 124by the rules engine 163. In some embodiments, the rules engine 163and/or compression rules database 124 are part of the compressioncomputing device (e.g. stored on the memory/storage 153 of thecompression computing device and/or are available via a local areanetwork coupled to the compression computing device 115). In otherembodiments, any of these methods may be performed by other computingdevices or systems. Depending on the embodiment, the methods illustratedin flowcharts may include fewer or additional blocks and/or the blocksmay be performed in an order different than is illustrated.

a. Example Method Including Generation of a Compressed Image

The flowchart of FIG. 6 illustrates an embodiment that may be used inconjunction with compression rules. The blocks are discussed withreference to the example compression rules illustrated in FIG. 5A, butother compression rules may be utilized.

At block 601, an image to be compressed is received, retrieved, and/oraccessed, such as from the image server 120, PACS 121, or any othersource. In one embodiment, image characteristics associated with theimage and/or an exam associated with the image and/or theuser/group/site may also be retrieved and/or otherwise determined. Theseimage characteristics may include, for example,

-   -   Modality, e.g., CT, MRI, PET, etc.    -   Technical parameters associated with the image, such as slice        thickness, image resolution, MRI sequence parameters, etc.    -   Clinical information, such as clinical indication, age, gender,        etc.    -   User, group, or site compression rule preferences.    -   And/or any other image characteristics as described above.

At block 605, compression rules relevant to the image and/or informationassociated with the image (e.g., image characteristics) or exam and/oruser/group/site are retrieved. For example, if the image were an imageor images associated with a CT of the Brain, example rules illustratedin FIG. 5A could be retrieved, including “Sequence of Compression” table504 and Quality Rules 506

At block 610, the initial compression parameters are retrieved. In theexample of table 504, the initial compression parameters are to use JPEG2000 lossy compression with a Quality Factor of 70.

At block 615, the image is compressed with the current compressionparameters, in this example using JPEG 2000 lossy compression with aQuality Factor of 70, as discussed in the prior block.

At block 620, the compressed image is compared to the original (e.g.,uncompressed) image and the difference is calculated to determine errorsrelated to compression. For example, differences between the originalimage and the compressed image may be calculated as described above inreference to FIGS. 2, 3A-3B, and 4.

At block 625, the errors calculated in at block 620 are compared to theretrieved Quality Rules to determine whether or not the compressed imageis acceptable (e.g., significant information of the compressed image isnot lost as a result of the compression). In the example of the QualityRules illustrated in table 506, in order for the compressed image to beacceptable all of the quality criteria listed must be met. For example,the second rule indicates that pixels with values between −3 and 13 inthe original image must have an error of <5 HU, e.g., the value of eachpixel in the compressed image must differ by less than 5 from thecorresponding pixels in the original image for pixels in the originalimage that have values of −3 to 13. These quality rules also indicateacceptable errors in pixels having other value ranges within theoriginal image.

If the quality of the compressed image is acceptable (e.g., all of thequality rules in table 506 are met by the compressed image), then atblock 635, the compressed image is utilized, for example by storing itor transmitting it.

If the quality is not acceptable, then at block 630, the nextcompression parameters to try are chosen. In the example of ‘Sequence ofCompression” table 504, the next compression parameters to try would beJPEG 2000 lossy compression with a Quality Factor of 80. Note that ifthe Quality Factors of 70, 80, and 90 are tried and the quality is notacceptable, the final parameter in the table is utilized, “JPEGlossless” which guarantees that there are no errors in the compressedimage.

After the next compression parameters are chosen, the sequence returnsto block 615.

If compression is acceptable, than at block 635, the compressed image isutilized, for example for transmission or storage.

In one embodiment, different compression parameters may be used for eachimage in a series and/or an exam. In another embodiment, the samecompression parameters are used for every image in a series or exam. Inthose cases, the highest quality compression, e.g., highest qualityfactor, that is required for every image in a series or exam is utilizedfor all images in a series or exam. This technique of using the samecompression parameters for all images in a series or exam can be appliedto other embodiments, such as those discussed with reference to FIGS. 7and 8.

Advantageously, according to the example of FIG. 6, the system mayefficiently generate a compressed image that satisfies quality rules(e.g., in which significant information is not lost). Compressed imagesare generated and tested in series such that, once a compressed imagesatisfies the quality criteria, the system need not continue and thecompressed image can be stored or sent. This process may save processorpower as many compressed images do not need to be generated. Further,generation of the compressed image is tied to the functioning of theprocessor of the computing system 150, as the compressed image isgenerated by analysis of each pixel of the image, and compression of thepixels of the image.

b. Example Method Including Generation of Multiple Compressed Images

FIG. 7 is another embodiment of a method for compressing images based onquality rules. The blocks illustrated are discussed with reference tothe example rules illustrated in FIG. 5B.

At block 705, an image or images are received, retrieved, and/oraccessed, such as from the image server 120, PACS 121, or any othersource.

At block 710, compression rules are retrieved that apply to the image orimages received (as described above).

At block 715, a set of compressed images is created based on thecompression rules. As shown in the example rules illustrate in FIG. 5B,Compression Set table 514 lists a set of nine compression techniques,ranging from JPEG lossy with a quality factor of 70 to JPEG losslesscompression, including compression by both JPEG and JPEG 2000compression algorithms. In the embodiment of FIG. 7, each of the listedcompression techniques and quality factors (and/or compression ratios orother compression parameters in other embodiments) are performed on theimage (or images), rather than performing one compression technique at atime until a suitable compression technique is identified, such as mightbe performed with the method of FIG. 6.

At block 720, each of the compressed images, in this example nine, arecompared to the original image to identify differences between theoriginal images and compressed images, such as by determining pixelerrors between the compressed images and the original image (asdescribed above).

At block 725, the errors within the compressed images are compared tothe Quality Rules 526 and the compressed image that meets these qualityrules (e.g., significant information of the compressed image is not lostas a result of the compression) and has the highest compression ratio(smallest size) is automatically selected.

At block 730, the image chosen in the prior block is utilized, forexample for transmission or storage.

Advantageously, according to the example of FIG. 7, the system mayefficiently generate a compressed image that satisfies quality rules(e.g., in which significant information is not lost). Multiplecompressed images are generated and tested in parallel such that amaximum compressed image (that satisfies the quality criteria) may bequickly determined, and the compressed image can be stored or sent. Thisprocess may save processor time as multiple compressed images do notneed to be generated in series, but may be generated in parallel.Further, generation of the compressed image is tied to the functioningof the processor of the computing system 150, as the compressed image isgenerated by analysis of each pixel of the image, and compression of thepixels of the image.

c. Example Method Including Generation of a Correction Image

FIG. 8 is another embodiment of a method for compressing images butmaintaining quality as defined by rules. The blocks illustrated arediscussed with reference to the example rules illustrated in FIG. 5C.

At block 805, an image or images are received, retrieved, and/oraccessed, such as from the image server 120, PACS 121, or any othersource.

At block 810, compression rules are retrieved that apply to the image orimages received (as described above).

At block 815, a set of compressed images is created based on thecompression rules. As shown in the example rules illustrated in FIG. 5C,Compression Set table 524 lists a set of nine compression techniques,ranging from JPEG lossy with a quality factor to 70 to JPEG 2000lossless compression.

At block 820, each of the compressed images, in this example nine, arecompared to the original image and the differences between the originalimages and compressed images are determined (as described above).

At block 825, the errors within the compressed images are compared tothe Quality Rules 526 and a set of Correction Images are generated. Therespective Correction Images are generated such that, when added to therespective compressed images, the Correction Images correct the errorsin the compressed images on a pixel by pixel basis (or based on multiplepixels or groups of pixels at a time, e.g., 4×4 groups of pixels, 6×6groups of pixels, or the like). Thus, for example, adding a CorrectionImage to its associated compressed image results in the original imageand, thus, would meet the Quality Rules.

In some embodiments, correction using the correction image may beapplied to only certain portions, structures, regions, or signalintensity regions of an image. In the example of Quality Rules 526, thecorrection image only applies to pixels that have signal intensities inthe original image in the range of 14-55 HU, approximately correlatingwith brain tissue, as illustrated in blue in the automatic segmentationimage 420 of FIG. 4.

As described below with reference to FIG. 9, images 952, 962, and 972 ofFIG. 9 illustrate the specific pixels that would contain information inthe example correction images. The majority of pixels in each correctionimage would be 0 so the correction images would be highly compressiblewith lossless techniques. The correction image may be compressed using alossless compression technique, for example ZIP, RLE, JPEG 2000lossless, or JPEG lossless. In one embodiment, the correction image iscompressed with a number of different lossless techniques and the onechosen is the one that results in the smallest size.

At block 830, the pair of lossy compressed original image and losslesscompressed correction image with the smallest size is automaticallyselected.

At block 835, the image pair chosen in block 830 is utilized, forexample for transmission or storage.

When the compressed image is to be used later, for example for viewingby a user, the image may be reconstructed by decompressing the image andcorrection image and then applying the correction image to the originalcompressed image, for example by adding the two images on a pixel bypixel bases.

In another embodiment, the original image may be modified beforecompression (preprocessed) so that once compressed using lossycompression, the compressed image would pass the quality rules. In oneembodiment, this could be an iterative process.

In an embodiment, portions of an image requiring a lower pixel errorrate, such as brain tissue, may be corrected by applying the braintissue segmented portions of the correction image to the compressedimage prior to display or transmission to a viewing device. In thisembodiment, only this selectively corrected image may be transmitted,stored, and/or displayed, rather than the pair of compressed image anderror image.

Advantageously, according to the example of FIG. 8, the system mayefficiently generate a compressed image (and/or a compressed image and acorrection image) that satisfies quality rules (e.g., in whichsignificant information is not lost). Multiple compressed images, andtheir respective correction images, are generated and tested in parallelsuch that a maximum compressed image (that satisfies the qualitycriteria) may be quickly determined, and the compressed image (and,optionally, the correction image) can be stored or sent. This processmay save processor time as multiple compressed images and correctionimages do not need to be generated in series, but may be generated inparallel. Further, generation of the compressed image is tied to thefunctioning of the processor of the computing system 150, as thecompressed image is generated by analysis of each pixel of the image,and compression of the pixels of the image.

d. Example Correction Images

FIG. 9 is a diagram illustrating various example compressed versions ofan example digital image, according to an embodiment of the presentdisclosure. FIG. 9 illustrates example compressed images 950, 960, and970, which are JPEG 2000 lossy compressed images with quality factors of70, 80, and 90, respectively.

Below to those images are respective correction images 952, 962, and972, where each pixel that would have a non-zero value is displayed aswhite and those with zero values are displayed in blue. Thus the exampleimages illustrate pixels in white that are to be corrected based on thequality rules; in this example, the example quality rules illustrated inFIG. 5C (e.g., range of 14-55 with error >=5 HU).

Below each correction image is displayed the number of pixels that arecorrected in the correction image and the percentage of images in eachlossy compressed image that require correction based on the qualityrules of FIG. 5C. For example, for the JPEG 2000 lossy compressed imagecompressed with a Quality Factor=90 (image 970), only 0.7% of the pixelsin the correction image 972 are non-zero, so that the correction imagewill be highly compressible with a lossless compression technique,adding relatively little in size to the pair of the lossy compressedimage and correction image.

VIII. Example Computing Systems

Referring again to FIG. 1, various configurations of the computingsystem 150 and network environment 100 may be used to implement and/oraccomplish the systems and methods disclosed herein. For example, thecomputing system 150 may be configured to display and/or enable a userto view and/or interact with various types of data including digitalimages and/or other types of information, as described above.

As described above, the computing system may take various forms. In oneembodiment, the computing system 150 may be an information displaycomputing device and/or system, a server, a computer workstation, adesktop computer, a Picture Archiving and Communication System (PACS)workstation, a laptop computer, a mobile computer, a smartphone, atablet computer, a wearable computer (for example, a head-mountedcomputer and/or a computer in communication with a head-mounteddisplay), a smartwatch, a mobile computer, a cell phone, a personaldigital assistant, a gaming system, a kiosk, an audio player, and/or anyother device that utilizes a graphical user interface, such as officeequipment, automobiles, airplane cockpits, household appliances,automated teller machines, self-service checkouts at stores, informationand other kiosks, ticketing kiosks, vending machines, industrialequipment, and/or a television, for example. In an embodiment thecomputing system 150 comprises one or more computing devices incommunication with one another.

The computing system 150 may include various components including, forexample, one or more processors 152, memory and/or data storage 153(including one or more software modules 151 and/or a rules engine 163(which may itself comprise a software module)), an operating system 154,a display 155, one or more input devices 156, and/or one or moreinterfaces 157. Each of the components of the computing system 150 maybe connected and/or in communication with each other using, for example,a standard based bus system. In different embodiments, the standardbased bus system could be Peripheral Component Interconnect (“PCI”), PCIExpress, Accelerated Graphics Port (“AGP”), Micro channel, SmallComputer System Interface (“SCSI”), Industrial Standard Architecture(“ISA”) and Extended ISA (“EISA”) architectures, for example. Inaddition, the functionality provided for in the components and modulesof computing system 150 (as described above and below) may be combinedinto fewer components and modules or further separated into additionalcomponents and modules.

In various embodiments the software modules 151 may providefunctionality as described above with reference to the various figures.For example, modules 151 of the computing system 150 may include userinput modules, image display modules, compression modules, rules enginemodules (for example, rules engine 163), user interface modules, and/orthe like. For example, the compression and/or rules engine modules mayimplement the functionality and techniques described above. Further, theimage display modules and/or the user interface modules may display userinterfaces, images, and/or other data on the display 155 in response touser inputs (as described in reference to various embodiments of thepresent disclosure). Further, the image display modules and/or the userinterface modules may be configured and/or designed to generate userinterface data useable for rendering the interactive user interfacesdescribed herein, such as a web application and/or a dynamic web pagedisplayed by a computing device. In various embodiments the userinterface data may be used by the computing system 150, and/orcommunicated to any other computing device, such that the example userinterfaces are displayed to a user. For example, the user interface datamay be executed by a browser (and/or other software program) accessing aweb service and configured to render the user interfaces based on theuser interface data.

The rules engine 163 may operate in conjunction with the other modulesto perform various functionality of the data navigation systemsdescribed above. For example, the rules engine 163 may determine, basedon one or more rules of the compressions rules database 124, to compressan image using a certain compression technique and/or using certaincompression parameters, as described above. As also described above,rules that may be executed by the rules engine 163 may include variousother types of rules, including segmentation rules.

As described below, the software modules 151 may include varioussoftware instructions, code, logic instructions, and/or the like thatmay be executed by the one or more processors 152 to accomplish thefunctionality described above. In other embodiments, software modules151 may reside on another computing device and/or system, such as a webserver or other server (for example, server 120) or other server, and auser may directly interact with a second computing device and/or systemthat is connected to the other computing device and/or system via acomputer network.

The computing system 150 may run an off-the-shelf operating system 154such as a Windows, Linux, MacOS, Android, or iOS, or mobile versions ofsuch operating systems. The computing system 150 may also run a morespecialized operating system which may be designed for the specifictasks performed by the computing system 150, or any other availableoperating system.

The computing system 150 may include one or more computer processors152, for example, hardware computer processors. The computer processors152 may include central processing units (CPUs), and may further includededicated processors such as graphics processor chips, or otherspecialized processors. The processors may be used to execute computerinstructions based on the software modules 151 to cause the computingsystem 150 to perform operations as specified by the modules 151. Thesoftware modules 151 may include, by way of example, components, such assoftware components, object-oriented software components, classcomponents and task components, processes, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables. For example, modules may include software code written ina programming language, such as, for example, Java, Objective-C, Swift,JavaScript, ActionScript, Visual Basic, HTML, Lua, C, C++, or C#. While“modules” are generally discussed herein with reference to software, anymodules may alternatively be represented in hardware or firmware. Invarious embodiments, the modules described herein refer to logicalmodules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

The computing system 150 may also include memory 153. The memory 153 mayinclude volatile data storage such as RAM or SDRAM. The memory may alsoinclude more permanent forms of storage such as a hard disk drive, aflash disk, flash memory, a solid state drive, or some other type ofnon-volatile storage, as described below.

The computing system 150 may also include or be interfaced to one ormore display devices that provide information to the users. Displaydevices 155 may include a video display, such as one or morehigh-resolution computer monitors, or a display device integrated intoor attached to a laptop computer, handheld computer, smartphone,smartwatch, wearable computer, computer tablet device, or medicalscanner. In other embodiments, the display device 155 may include anLCD, OLED, or other thin screen display surface, a monitor, television,projector, a display integrated into wearable glasses, or any otherdevice that visually depicts user interfaces and data to viewers. Asdescribed above, images and other information may be displayed to theuser via the display devices 155 such that the user may efficiently viewand interact with such images and information.

The computing system 150 may also include or be interfaced to one ormore input devices 156 which receive input from users, such as akeyboard, trackball, mouse, 3D mouse, dial and/or knob (for example, asmartwatch crown), drawing tablet, joystick, game controller, touchsensitive surface (for example, capacitive or resistive touch screen),touchpad, accelerometer, video camera and/or microphone.

The computing system 150 may also include one or more interfaces 157which allow information exchange between the computing system 150 andother computers and input/output devices using systems such as Ethernet,Wi-Fi, Bluetooth, as well as other wired and wireless datacommunications techniques.

In various embodiments, the functionality provided by the imagingdevice/scanner 110, the PACS 121, the PACS workstation 122, the imageserver 120, and/or the compression rules database 124, may reside withincomputing system 150.

The computing system 150 may communicate and/or interface with othersystems and/or devices. In one or more embodiments, the computing system150 may be connected to the computer network 190. The computer network190 may take various forms. For example, the computer network 190 may bea wired network or a wireless network, or it may be some combination ofboth. The computer network 190 may be a single computer network, or itmay be a combination or collection of different networks and networkprotocols. Additionally, the computer network 190 may include one ormore local area networks (LAN), wide area networks (WAN), personal areanetworks (PAN), cellular or data networks, and/or the Internet.

Various devices and subsystems may be connected to the network 190. Asshown in FIG. 1, for example, the computing system 150 may be incommunication with the imaging device/scanner 110, the PACS 121, thePACS workstation 122, the image server 120, and/or the compression rulesdatabase 124. Image server 120 include a database, data store, and/orother electronic or computer-readable medium storage device configuredto store, for example, digital images and/or other data. Such imagesand/or other data may be processed, for example, by the server 120and/or the computing system 150. Further, the various components of thenetwork environment 100 may be in communication with various otherdevices that may, for example, capture and provide images and/or otherdata to the computing system 150. For example, imaging device/scanner110 may include one or more medical scanners may be connected, such asMRI scanners. The MRI scanner may be used to acquire MRI images frompatients, and may share the acquired images with other devices on thenetwork 190. The imaging device/scanner 110 may also include one or moreCT scanners and/or X-Ray scanners. The CT scanners and/or X-Ray scannersmay also be used to acquire images and, like the MRI scanner, may thenstore those images and/or share those images with other devices via thenetwork 190. Any other scanner or device capable of inputting orgenerating information that may be presented to the user as images,graphics, text, sound, video, etc. may be connected to the network 190,including, for example, computing systems used in the fields ofultrasound, angiography, nuclear medicine, radiography, endoscopy,pathology, dermatology, and the like.

Also connected to the network 190 may be a Picture Archiving andCommunications System (PACS) 121 and/or PACS workstation 122. The PACSSystem 121 may be used for the storage, retrieval, distribution andpresentation of images (such as those created and/or generated by theMRI scanner and/or CT Scanner). The medical images may be stored in anindependent format, an open source format, or some other proprietaryformat. A common format for image storage in the PACS system is theDigital Imaging and Communications in Medicine (DICOM) format. Invarious embodiments, the stored images may be transmitted digitally viathe PACS system, often reducing or eliminating the need for manuallycreating, filing, or transporting film jackets.

The network 190 may also be connected to a Radiology Information System(RIS). In an embodiment, the radiology information system may be acomputerized system that is used by radiology departments to store,manipulate and distribute patient radiological information.

Also attached to the network 190 may be an Electronic Medical Record(EMR) system. The EMR system may be configured to store and makeaccessible to a plurality of medical practitioners computerized medicalrecords. Also attached to the network 190 may be a LaboratoryInformation System. In an embodiment, the Laboratory Information Systemmay be a software system which stores information created or generatedby clinical laboratories. Also attached to the network 190 may be aDigital Pathology System that may be used to digitally manage and storeinformation related to medical pathology.

Also attached to the network 190 may be one or more Computer AidedDiagnosis Systems (CAD) systems that are generally used to performComputer-Aided Processing (CAP) such as, for example, CAD processes. Inone embodiment, the CAD systems functionality may reside in a computingdevice and/or system separate from computing system 150 while in anotherembodiment the CAD systems functionality may reside within computingsystem 150.

Also attached to the network 190 may be one or more Processing Systemsthat may be used to perform computerized advanced processing such as,for example, computations on imaging information to create new views ofthe information, for example, volume rendering and/or other types ofprocessing, for example image enhancement, volume quantification,blood-flow quantification, and the like. In one embodiment, suchprocessing functionality may reside in a computing device and/or systemseparate from computing system 150 while in another embodiment theprocessing functionality may reside within computing system 150.

In other embodiments, other computing devices and/or systems that store,provide, acquire, and/or otherwise manipulate medical data may also becoupled to the network 190 and may be in communication with one or moreof the devices illustrated in FIG. 1, such as with the computing system150.

Depending on the embodiment, other devices discussed herein may includesome or all of the same components discussed above with reference to thecomputing system 150 and may perform some or all of the functionalitydiscussed herein.

As mentioned above, various of the components of the network environment100 of FIG. 1 described above may or may not be considered a part of thecomputing system 150. For example, in some embodiments one or more ofthese components may be implemented as part of the computing system 150,may be in direct communication with the computing system 150, and/or maybe in indirect communication (e.g., over network 190) with the computingsystem 150.

IX. Additional Embodiments

Any of the processes, methods, algorithms, elements, blocks,applications, or other functionality (or portions of functionality)described in the preceding sections may be embodied in, and/or fully orpartially automated via, modules, segments, and/or portions of softwarecode and/or logic instructions which include one or more executableinstructions (as described below) executed by one or more computersystems or computer processors comprising computer hardware. Further,and/or alternatively, any of the processes, methods, algorithms,elements, blocks, applications, or other functionality (or portions offunctionality) described in the preceding sections may be embodied in,and/or fully or partially automated via, electronic hardware such asapplication-specific processors (e.g., application-specific integratedcircuits (ASICs)), programmable processors (e.g., field programmablegate arrays (FPGAs)), application-specific circuitry, logic circuits,and/or the like (any of which may also combine custom hard-wired logic,ASICs, FPGAs, etc. with custom programming/execution of softwareinstructions to accomplish the techniques). For example, the variousillustrative logical blocks, methods, routines, and the like describedin connection with the embodiments disclosed herein may be implementedas electronic hardware, computer software, or combinations of both. Toillustrate this, various illustrative components, blocks, modules, andsteps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. The described functionality may beimplemented in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the disclosure.

For example, the functionality described herein may be performed assoftware instructions are executed by, and/or in response to softwareinstruction being executed by, one or more hardware processors and/orany other suitable computing devices. The software instructions and/orother executable code may be read from a non-transitory or tangiblecomputer-readable medium.

The terms “non-transitory medium,” “non-transitory computer-readablemedium,” “tangible computer-readable storage medium,” and similar terms,as used herein are synonymous with the term “data store,” and are broadterms encompassing their ordinary and customary meanings, and includeany data stores and/or mediums that store data and/or instructions thatcause a machine (e.g., a computing device) to operate in a specificfashion. Such non-transitory mediums may comprise non-volatile mediumsand/or volatile mediums. Non-volatile mediums include, for example,optical or magnetic disks. Volatile mediums include, for example,dynamic memory (e.g., random-access memory (RAM)). Common forms ofnon-transitory mediums include, for example, floppy disks, flexibledisks, hard disks, solid state drives, magnetic tape, or any othermagnetic data storage medium, a CD-ROM, a DVD-ROM, any other opticaldata storage medium, any physical medium with patterns of holes, a RAM,a PROM, an EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge, and networked versions of the same. Non-transitory mediumsare distinct from, but may be used in conjunction with, transmissionmediums. Transmission mediums participate in transferring informationbetween non-transitory mediums. For example, transmission mediumsinclude coaxial cables, copper wire, and fiber optics, including wiresthat comprise busses and/or the like within certain computing devices.Transmission mediums may also take the form of acoustic or light waves,such as those generated during radio-wave and infra-red datacommunications.

Accordingly, a software instruction and/or module may reside in RAMmemory, flash memory, ROM memory, hard disk, solid state drive, CD-ROM,DVD-ROM, and/or any other form of a non-transitory computer-readablestorage medium. Various forms of mediums may be involved in carrying oneor more sequences of one or more instructions to computer processors (ofthe present disclosure) for execution. For example, the instructions mayinitially be carried on a magnetic disk or solid state drive of a remotecomputer. The remote computer may load the instructions and/or modulesinto its dynamic memory and send the instructions over a telephone,cable, or optical line using a modem. A modem local to a servercomputing system may receive the data on the telephone/cable/opticalline and use a converter device including the appropriate circuitry toplace the data on a bus. The bus may carry the data to a memory, fromwhich a processor may retrieve and execute the instructions. Theinstructions received by the memory may optionally be stored on astorage device (e.g., a solid state drive) either before or afterexecution by the computer processor.

Any of the above-mentioned processors, and/or devices incorporating anyof the above-mentioned processors, may be referred to herein as, forexample, “computer devices,” “computing devices,” “hardware computingdevices,” “hardware processors,” “processing units,” and/or the like.Computing devices of the above-embodiments may generally (but notnecessarily) be controlled and/or coordinated by operating systemsoftware, such as Mac OS, iOS, Android, Chrome OS, Windows OS (e.g.,Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10, WindowsServer, etc.), Windows CE, Unix, Linux, SunOS, Solaris, Blackberry OS,VxWorks, or other suitable operating systems. In other embodiments, thecomputing devices may be controlled by a proprietary operating system.Conventional operating systems control and schedule computer processesfor execution, perform memory management, provide file system,networking, I/O services, and provide a user interface functionality,such as a graphical user interface (“GUI”), among other things.

As described above, in various embodiments certain functionality may beaccessible by a user through a web-based viewer (such as a web browser),or other suitable software program. In such implementations, the userinterface may be generated by a server computing system and transmittedto a web browser of the user (e.g., running on the user's computingsystem). Alternatively, data (e.g., user interface data) necessary forgenerating the user interface may be provided by the server computingsystem to the browser, where the user interface may be generated (e.g.,the user interface data may be executed by a browser accessing a webservice and may be configured to render the user interfaces based on theuser interface data). The user may then interact with the user interfacethrough the web browser. User interfaces of certain implementations maybe accessible through one or more dedicated software applications. Incertain embodiments, one or more of the computing devices and/or systemsof the disclosure may include mobile computing devices, and userinterfaces may be accessible through such mobile computing devices (forexample, smartphones and/or tablets).

In general, the terms “code,” “instructions,” “module,” “application,”“software application,” and/or the like, as used herein, refer to acollection of software instructions, possibly having entry and exitpoints, written in a programming language, such as, for example, Java,Lua, C or C++. Such software may be compiled and linked into anexecutable program, installed in a dynamic link library, or may bewritten in an interpreted programming language such as, for example,BASIC, Perl, or Python. It will be appreciated that such softwareinstructions may be callable from other software instructions or fromitself, and/or may be invoked in response to detected events orinterrupts. Software instructions configured for execution on computingdevices may be provided on a computer readable medium (e.g., anon-transitory computer readable medium), and/or as a digital download(and may be originally stored in a compressed or installable format thatrequires installation, decompression or decryption prior to execution)that may then be stored on a computer readable medium (e.g., anon-transitory computer readable medium). Such software instructions maybe stored, partially or fully, on a memory device (e.g., anon-transitory computer readable medium) of the executing computingdevice, for execution by the computing device.

Alternate implementations are included within the scope of theembodiments described herein in which certain elements or functions maybe deleted, executed out of order from that shown or discussed,including substantially concurrently (for example, throughmulti-threaded processing, interrupt processing, or multiple processorsor processor cores or on other parallel architectures) or in reverseorder, depending on the functionality involved. Further, the variousfeatures and processes described above may be used independently of oneanother, or may be combined in various ways. All possible combinationsand subcombinations are intended to fall within the scope of thisdisclosure. In addition, certain method or process blocks may be omittedin some implementations. The methods and processes described herein arealso not limited to any particular sequence, and the blocks or statesrelating thereto can be performed in other sequences that areappropriate. For example, described blocks or states may be performed inan order other than that specifically disclosed, or multiple blocks orstates may be combined in a single block or state. The example blocks orstates may be performed in serial, in parallel, or in some other manner.Blocks or states may be added to or removed from the disclosed exampleembodiments. The example systems and components described herein may beconfigured differently than described. For example, elements may beadded to, removed from, or rearranged compared to the disclosed exampleembodiments.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments. It will be appreciated, however, that no matter howdetailed the foregoing appears in text, the systems and methods can bepracticed in many ways. As is also stated above, it should be noted thatthe use of particular terminology when describing certain features oraspects of the systems and methods should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the systems andmethods with which that terminology is associated.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Conjunctive language such as the phrase “at least one of X, Y, and Z,”or “at least one of X, Y, or Z,” unless specifically stated otherwise,is to be understood with the context as used in general to convey thatan item, term, etc. may be either X, Y, or Z, or a combination thereof.For example, the term “or” is used in its inclusive sense (and not inits exclusive sense) so that when used, for example, to connect a listof elements, the term “or” means one, some, or all of the elements inthe list. Thus, such conjunctive language is not generally intended toimply that certain embodiments require at least one of X, at least oneof Y, and at least one of Z to each be present.

The term “a” as used herein should be given an inclusive rather thanexclusive interpretation. For example, unless specifically noted, theterm “a” should not be understood to mean “exactly one” or “one and onlyone”; instead, the term “a” means “one or more” or “at least one,”whether used in the claims or elsewhere in the specification andregardless of uses of quantifiers such as “at least one,” “one or more,”or “a plurality” elsewhere in the claims or specification.

The term “comprising” as used herein should be given an inclusive ratherthan exclusive interpretation. For example, a general purpose computercomprising one or more processors should not be interpreted as excludingother computer components, and may possibly include such components asmemory, input/output devices, and/or network interfaces, among others.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it may beunderstood that various omissions, substitutions, and changes in theform and details of the devices or processes illustrated may be madewithout departing from the spirit of the disclosure. As may berecognized, certain embodiments of the inventions described herein maybe embodied within a form that does not provide all of the features andbenefits set forth herein, as some features may be used or practicedseparately from others. The scope of certain inventions disclosed hereinis indicated by the appended claims rather than by the foregoingdescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

What is claimed is:
 1. A method of digital image compression, the methodcomprising: causing execution of software instructions by one or morehardware computing devices in order to: receive an uncompressed digitalimage; determine a characteristic associated with the uncompresseddigital image; access a compression rule associated with thecharacteristic; compress, based on the compression rule, theuncompressed digital image to generate a first compressed digital image;determine a difference between at least a portion the uncompresseddigital image and a corresponding portion of the first compresseddigital image to generate difference image data; determine, based on thedifference image data, a first amount of errors in the first compresseddigital image; compare the first amount of errors to an error thresholdincluded in the compression rule; in response to determining that thefirst amount of errors exceeds the error threshold, recompress, based onthe compression rule, the uncompressed digital image to generate asecond compressed digital image having a level of compression less thanthe first compressed digital image; and determine a second amount oferrors based on comparison of the second compressed digital image to theuncompressed digital image.
 2. The method of claim 1, wherein thecharacteristic associated with the uncompressed digital image includesat least one of: an imaging modality, an anatomical feature, or anacquisition type.
 3. The method of claim 1, wherein the compression ruleindicates a compression algorithm and a first quality factor, and thefirst compressed digital image is generated based on the compressionalgorithm and the first quality factor.
 4. The method of claim 3,wherein the compression rule indicates a second quality factor that isgreater than the first quality factor, and the second compressed digitalimage is generated based on the compression algorithm and a secondquality factor.
 5. The method of claim 1, wherein determining the firstamount of errors comprises at least one of: determining a number ofpixels in the difference image data having a value indicative of anerror, or determining a degree of error in one or more pixels of thedifference image data.
 6. The method of claim 1 further comprising:causing execution of software instructions by one or more hardwarecomputing devices in order to: identify one or more pixels of theuncompressed digital image having an intensity value satisfying athreshold; designate the one or more pixels as the portion of theuncompressed digital image; for each pixel of the one or more pixels,determine a difference between the pixel of the uncompressed digitalimage and the corresponding pixel of the first compressed digital imageto generate the difference image data; and determine the first amount oferrors by at least one of: determining a number of pixels in thedifference image data having a value indicative of an error, ordetermining a degree of error in one or more pixels of the differenceimage data.
 7. The method of claim 6, wherein each of the one or morepixels comprises a group of pixels, and wherein each group of pixelscomprises at least one of: a 4×4 group of pixels, or a 6×6 group ofpixels.
 8. The method of claim 1 further comprising: causing executionof software instructions by one or more hardware computing devices inorder to: identify one or more regions of the uncompressed digital imagehaving pixel intensity values satisfying a threshold; designate the oneor more regions as the portion of the uncompressed digital image; foreach pixel or group of pixels of the one or more regions, determine adifference between the pixel of the uncompressed digital image and thecorresponding pixel of the first compressed digital image to generatethe difference image data; and determine the first amount of errors byat least one of: determining a number of pixels in the difference imagedata having a value indicative of an error, or determining a degree oferror in one or more pixels of the difference image data.
 9. The methodof claim 1 further comprising: causing execution of softwareinstructions by one or more hardware computing devices in order to:store the second compressed digital image in a data store.
 10. Themethod of claim 1 further comprising: causing execution of softwareinstructions by one or more hardware computing devices in order to: inresponse to determining that the second amount of errors exceeds theerror threshold defined by the compression rule, recompress theuncompressed digital image using a lossless compression technique togenerate a third compressed digital image; and store the thirdcompressed digital image in a data store.
 11. A non-transitorycomputer-readable storage medium storing software instructions that, inresponse to execution by a computer system having one or more hardwareprocessors, configure the computer system to perform operationscomprising: receiving an uncompressed digital image; determining acharacteristic associated with the uncompressed digital image; accessinga compression rule associated with the characteristic; compressing,based on the compression rule, the uncompressed digital image togenerate a first compressed digital image; determining a differencebetween at least a portion the uncompressed digital image and acorresponding portion of the first compressed digital image to generatedifference image data; determining, based on the difference image data,a first amount of errors in the first compressed digital image;comparing the first amount of errors to an error threshold included inthe compression rule; in response to determining that the first amountof errors exceeds the error threshold, recompressing, based on thecompression rule, the uncompressed digital image to generate a secondcompressed digital image having a level of compression less than thefirst compressed digital image; and determining a second amount oferrors based on comparison of the second compressed digital image to theuncompressed digital image.
 12. The non-transitory computer-readablestorage medium of claim 11, wherein the compression rule indicates acompression algorithm and a first compression ratio, and the firstcompressed digital image is generated based on the compression algorithmand the first compression ratio.
 13. The non-transitorycomputer-readable storage medium of claim 12, wherein the compressionrule indicates a second compression ratio that is greater than the firstcompression ratio, and the second compressed digital image is generatedbased on the compression algorithm and a second compression ratio. 14.The non-transitory computer-readable storage medium of claim 11, whereindetermining the first amount of errors comprises at least one of:determining a number of pixels in the difference image data having avalue indicative of an error, or determining a degree of error in one ormore pixels of the difference image data.
 15. The non-transitorycomputer-readable storage medium of claim 11, wherein the softwareinstructions further configure the computer system to perform operationscomprising: identifying one or more pixels of the uncompressed digitalimage having an intensity value satisfying a threshold; designating theone or more pixels as the portion of the uncompressed digital image; foreach pixel of the one or more pixels, determining a difference betweenthe pixel of the uncompressed digital image and the corresponding pixelof the first compressed digital image to generate the difference imagedata; and determining the first amount of errors by at least one of:determining a number of pixels in the difference image data having avalue indicative of an error, or determining a degree of error in one ormore pixels of the difference image data.
 16. The non-transitorycomputer-readable storage medium of claim 15, wherein each of the one ormore pixels comprises a group of pixels, and wherein each group ofpixels comprises at least one of: a 4×4 group of pixels, or a 6×6 groupof pixels.
 17. A computer system comprising: one or more hardwarecomputer processors configured to execute software instructions in orderto at least: receive an uncompressed digital image; determine acharacteristic associated with the uncompressed digital image; access acompression rule associated with the characteristic; compress, based onthe compression rule, the uncompressed digital image to generate a firstcompressed digital image; determine a difference between at least aportion the uncompressed digital image and a corresponding portion ofthe first compressed digital image to generate difference image data;determine, based on the difference image data, a first amount of errorsin the first compressed digital image; compare the first amount oferrors to an error threshold included in the compression rule; inresponse to determining that the first amount of errors exceeds theerror threshold, recompress, based on the compression rule, theuncompressed digital image to generate a second compressed digital imagehaving a level of compression less than the first compressed digitalimage; and determine a second amount of errors based on comparison ofthe second compressed digital image to the uncompressed digital image.18. The computer system of claim 17, wherein the one or more hardwarecomputer processors are further configured to execute softwareinstructions in order to at least: identify one or more regions of theuncompressed digital image having pixel intensity values satisfying athreshold; designate the one or more regions as the portion of theuncompressed digital image; for each pixel or group of pixels of the oneor more regions, determine a difference between the pixel of theuncompressed digital image and the corresponding pixel of the firstcompressed digital image to generate the difference image data; anddetermine the first amount of errors by at least one of: determining anumber of pixels in the difference image data having a value indicativeof an error, or determining a degree of error in one or more pixels ofthe difference image data.
 19. The computer system of claim 17, whereinthe one or more hardware computer processors are further configured toexecute software instructions in order to at least: store the secondcompressed digital image in a data store.
 20. The computer system ofclaim 17, wherein the one or more hardware computer processors arefurther configured to execute software instructions in order to atleast: in response to determining that the second amount of errorsexceeds the error threshold defined by the compression rule, recompressthe uncompressed digital image using a lossless compression technique togenerate a third compressed digital image; and store the thirdcompressed digital image in a data store.